P13 The effect of a standardised procedural checklist on assessment in simulation-based mastery learning

Schnieke-Kind, CA; Hampton, Eric M.; Tiernan, James

doi:10.1136/bmjstel-2017-aspihconf.101

Cited by 1 publication

(1 citation statement)

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“…Although the key feature data obtained by positioning can accurately obtain the information of the image unit where the face is located, due to some objective reasons, such as the angle of the photo, light, and darkness, the collected key feature data will eventually be incomplete and will directly affect the final comparison result. erefore, key localised data need to be calibrated or complemented twice to match with the target library data under the same comparison criteria, so as to improve the correct recognition rate [18].…”

Section: Facial Data Calibrationmentioning

confidence: 99%

Intelligent Classroom Teaching Assessment System Based on Deep Learning Model Face Recognition Technology

Zhang

Dolah

2022

Scientific Programming

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AI + education technology has had a wide and far-reaching impact on the education industry, which has been applied to classroom teaching in order to achieve real-time monitoring of the classroom and complete closed-loop feedback on teaching. In this paper, AI face recognition based on deep learning is improved and optimized through key points such as image data acquisition, localization, calibration, comparison, and deepening data models, so that the detection can be completed accurately and quickly even under the condition of light and external object interference, maximizing the accuracy and efficiency of face recognition technology under the influence of the objective environment. The traditional manual operation in classroom teaching is automated and intelligent throughout, reducing the workload of teachers and related personnel while further ensuring the scientific organization and management of classroom teaching. While making classroom teaching and learning life safer, more convenient, and more efficient, it also provides a favourable reference for the wide application and development of facial recognition technology.

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Section: Facial Data Calibrationmentioning

confidence: 99%